Methods and systems for custom metadata driven data protection and identification of data

ABSTRACT

Systems and methods for classification of backup data are disclosed. The methods include maintaining a plurality of backup data storage systems in communication with an external metadata management system, and operating the metadata management system to store tagged metadata corresponding to backup data residing on the plurality of backup data storage systems. The tagged metadata includes a plurality of custom tags indicative of information about the backup data.

BACKGROUND

The disclosure herein relates generally to a data backup methods andsystems. Specifically, the present disclosure relates to a system forclassification of backup data stored in backup storage systems.

Users and organizations that deal with significant quantities of digitalinformation often have difficulty managing files and data in anefficient and intuitive manner. An inability to easily store, organize,and locate documents and content, while causing difficulty andirritation at the level of the individual user, may translate intosignificant inefficiencies and lost opportunities at the organizationallevel. Lost documents, overlooked e-mails and records, and theduplication of work between users or departments may impact a business'sproductivity and agility. For the digital consumer, difficultyorganizing and locating digital data may result in user frustration andthe accidental re-purchasing of extant content.

Modern high-capacity hard drives and remote storage solutions allow forthe retention of large numbers of documents and records nearlyindefinitely; however, increases in storage capacity have often not beenaccompanied by a corresponding increase in the effectiveness of documentmanagement tools and technology. Most modern storage solutions utilizesome combination of a traditional directory-based file system andsearch-based data management such as full-text search or basic keywordtagging. Although appropriate for some types of data, both types ofsystems may present significant challenges when dealing with largenumbers of files or heterogeneous data sets. Directory-based solutionsmay be satisfactory for highly structured data or content; however,directory-trees often break down as an organizational method when adocument or datum is relevant across one or more data categories or whena user desires to cross-reference or locate documents based on analternate organizational schema. Simple text and keyword search-basedsystems generally discard the rigid structure of the directory-tree, butmay present other challenges, such as requiring that the user rememberspecific terms or phrases associated with the document to be located.The lack of structure associated with many keyword or full-text baseddata management solutions may also pose difficulties when similarkeyword terms occur over different classes of documents, such as a“flight” keyword being used both for trip records and engineeringdocuments.

Some of the weaknesses with directory and keyword/text search-basedsystems may be mitigated by associating metadata with each piece ofdata. Metadata is broadly defined as “data about data” i.e. a label ordescription. Thus, a given item of metadata may be used to describe anindividual datum, or a content item, and/or a collection of data whichcan include a plurality of content items. The fundamental role ofmetadata is to facilitate or aid in the understanding, use andmanagement of data. The metadata required for efficient data managementis dependent on, and varies with, the type of data and the context ofuse of this data. Using as an example a library, the data is the contentof the titles stocked, and the metadata about a title would typicallyinclude a description of the content, and any other information relevantfor whatever purposes, for example the publication date, author,location in the library, etc. For photographic images, metadatatypically labels the date the photograph was taken, whether day orevening, the camera settings, and information related to copyrightcontrol, such as the name of the photographer, and owner and date ofcopyright. Therefore, the metadata may have clearer semantics andinclude some category information to organize the data in therepository. Even more, the relationships among different metadata itemsmay be involved to describe more complex semantics. Obviously, the queryon metadata is more effective to retrieve appropriate results than thefull-text search, especially for some specific areas difficult to applythe full-text search, such as multimedia.

To protect data holdings from being lost, there is a need for a regularprocess in which the data is saved or backed up on a data storage media.This regular process of saving data is often referred to as performing a“backup”. Because of the increasing volumes of data being stored and themore insistent demands for data security, the amount of data beingbacked up, the frequency of scheduled backups continues to increase inmany systems, and associated complexity of performing the backups.Restoration or access to such backup data requires accessing the largeamounts of backup data stored on the backup data storage systems andidentifying the required data sets, which requires huge processing timesand resources.

SUMMARY

The summary of the disclosure is given to aid understanding of a datastorage system, data storage system architectural structure, processor,and method of encrypting contents of a data storage system, and not withan intent to limit the disclosure or the invention. The presentdisclosure is directed to a person of ordinary skill in the art. Itshould be understood that various aspects and features of the disclosuremay advantageously be used separately in some instances, or incombination with other aspects and features of the disclosure in otherinstances. Accordingly, variations and modifications may be made to thecomputer system, the architectural structure, processor, and theirmethod of operation to achieve different effects.

Systems and methods for classification of backup data are disclosed.According to an embodiment of the present disclosure, the methodsinclude maintaining a plurality of backup data storage systems incommunication with an external metadata management system, and operatingthe metadata management system to store tagged metadata corresponding tobackup data residing on the plurality of backup data storage systems.The tagged metadata includes a plurality of custom tags indicative ofinformation about the backup data.

In certain embodiments, operating the metadata management system tostore tagged metadata corresponding to backup data residing on theplurality of backup data storage systems may include receiving initialscan metadata from one or more of the plurality of backup data storagesystems, adding a tag to the received metadata to form tagged metadata,and storing the tagged metadata. Optionally, adding the tag to thereceived initial scan metadata may include receiving, from a user, atleast one policy that includes a plurality of classification rules, andat least one tag associated with each of the plurality of classificationrules. Adding the tag may also include analyzing the received initialmetadata to determine if the received initial scan metadata satisfiesone or more of the plurality of classification rules, and adding the atleast one tag associated with each of the one or more of the pluralityof classification rules with the received initial scan metadata inresponse to determining that the received initial scan metadatasatisfies one or more of the plurality of classification rules.Additionally and/or alternatively, adding the tag to the receivedinitial scan metadata may also include receiving initial scan metadatafrom one or more of the plurality of backup data storage systems,extracting one or more components of the received metadata, using atleast one annotator to identify facets for one or more of the entries inthe stored metadata, classifying the metadata based on the identifiedfacets, and adding tags to the received metadata.

In one or more embodiments, operating the metadata management system tostore tagged metadata corresponding to backup data residing on theplurality of backup data storage systems may also include receivingreal-time metadata, adding a tag to the received metadata to form taggedmetadata, and storing the tagged metadata. Optionally, receivingreal-time metadata may include receiving event metadata upon executionof a data operation on one or more of the plurality of backup datastorage systems.

In other embodiments, operating the metadata management system to storetagged metadata corresponding to backup data residing on the pluralityof backup data storage systems may include extracting facets from thebackup data residing on the plurality of backup data storage systems.Alternatively and/or additionally, the methods may also includeclassifying the metadata based on the identified facets, and adding tagsto the received metadata.

The methods may also include inheriting tags from metadata associatedwith original content from which the backup data is created. Inheritingthe tags may include identifying one or more source custom tags includedin metadata corresponding to the original content, and in response todetecting execution of a backup operation on the original content,storing the one or more source custom tags as tags for backup datacreated by the execution of the backup operation.

In one or more embodiments, the methods may be performed by a processorexecuting instructions included on a non-transitory computer readablemedium.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The various aspects, features and embodiments of a computer system,computer architectural structure, processor, and their method ofoperation will be better understood when read in conjunction with thefigures provided. Embodiments are provided in the figures for thepurpose of illustrating aspects, features, and/or various embodiments ofthe computer system, computer architectural structure, processors, andtheir method of operation, but the claims should not be limited to theprecise arrangement, structures, features, aspects, assemblies, subassemblies, systems, circuitry, embodiments, or devices shown, and thearrangements, structures, subassemblies, assemblies, features, aspects,methods, processes, circuitry, embodiments, and devices shown may beused singularly or in combination with other arrangements, structures,assemblies, subassemblies, systems, features, aspects, circuitry,embodiments, methods and devices.

FIG. 1 depicts one example of a computing environment, according toembodiments of the present disclosure.

FIG. 2 is a functional block diagram illustrating a computer system,according to embodiments of the present disclosure.

FIG. 3 depicts an example block diagram of an information managementsystem, according to embodiments of the present disclosure.

FIG. 4 is a functional block diagram illustrating a backup module,according to embodiments of the present disclosure.

FIG. 5 is an exemplary flowchart illustrating and describing a methodfor intelligent backup of data in data storage systems according toembodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is made for illustrating the generalprinciples of the invention and is not meant to limit the inventiveconcepts claimed herein. In the following detailed description, numerousdetails are set forth in order to provide an understanding of thecomputer system, computer architectural structure, storage systems,processor, and their method of operation, however, it will be understoodby those skilled in the art that different and numerous embodiments ofthe computer system, computer architectural structure, storage systems,processor, and their methods of operation may be practiced without thosespecific details, and the claims and disclosure should not be limited tothe embodiments, assemblies subassemblies, assemblies, processes,methods, aspects, or details specifically described and shown herein.Further, particular features described herein can be used in combinationwith other described features in each of the various possiblecombinations and permutations.

Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc. It must also benoted that, as used in the specification and the appended claims, thesingular forms “a,” “an” and “the” include plural referents unlessotherwise specified, and that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

As used herein, the term “content” or “data” means any computer-readabledata including, but not limited to, digital photographs, digitizedanalog photos, music files, video clips, text documents, interactiveprograms, web pages, word processing documents, computer assisted designfiles, blueprints, flowcharts, invoices, database reports, databaserecords, video game assets, sound samples, transaction log files,electronic documents, files which simply name other objects, and thelike. The content may be organized and stored in the form of objects,files, blocks, or any other suitable format in one or more data storagesystems, and can include files, directories, file system volumes, datablocks, extents, or any other hierarchies or organizations of datablocks. As used herein, a “data set” can refer to (1) any file that iscurrently addressable by a file system or that was previouslyaddressable by the file system (e.g., an archive file) and/or (2) asubset of such a file (e.g., a data block). Data may include structureddata (e.g., database files), unstructured data (e.g., documents), and/orsemi-structured data. Specifically, a data set can be a file, directory,share, volume, region within a volume, or an embedded object. Data setscan be complex, containing other embedded objects. For example, a filecan be a container containing other files, or a volume can have a filesystem on top of it which in turn contains files. The system is capableof recognizing complex objects and tracking changes at finer embeddedobject granularity.

A “container” may be a data set which may have other embedded objects,such as a file, directory, file system, or volume.

As used herein, the term “metadata” refers to any descriptive oridentifying information in computer-processable form that is associatedwith particular content or data set. Generally speaking, content willhave metadata that is relevant to a number of characteristics of thecontent and the overall content collection (e.g., a file), including,but not limited to, the content's technical aspects (format, bytes used,date of creation), the workflow in which the content participates(creator, owner, publisher, date of publication, copyright information,etc) and the subject matter of the content (the nature of the sound ofan audio file, be it music or a sound-effect, the subject of aphotograph or video clip, the abstract of a lengthy text document,excerpted particulars of invoices or other data-interchange formatfiles). For example, metadata items may include but are not limited toone or more of the following: the content owner (e.g., the client oruser that generates the content), the last modified time (e.g., the timeof the most recent modification of a data set), a data set name (e.g., afile name), a data set size (e.g., a number of bytes of data set),information about the content (e.g., an indication as to the existenceof a particular search term), user-supplied tags, to/from informationfor email (e.g., an email sender, recipient, etc.), creation date, filetype (e.g., format or application type), last accessed time, applicationtype (e.g., type of application that generated the data block),location/network (e.g., a current, past or future location of the dataset and network pathways to/from the data block), geographic location(e.g., GPS coordinates), frequency of change (e.g., a period in whichthe data set is modified), business unit (e.g., a group or departmentthat generates, manages or is otherwise associated with the set), aginginformation (e.g., a schedule, such as a time period, in which the dataset is migrated to secondary or long term storage), boot sectors,partition layouts, file location within a file folder directorystructure, user permissions, owners, groups, access control lists[ACLS]), system metadata (e.g., registry information), combinations ofthe same or the other similar information related to the data set.

The term “metadata tag” or “tag” refers to any descriptive oridentifying information in computer-processable form that is associatedwith particular metadata, and that is indicative of the actualinformation of the content included in various data storage systems andwith which the metadata is associated.

The following discussion omits or only briefly describes conventionalfeatures of data storage systems and information processing systems,including processors and microprocessor systems and architectures, whichare apparent to those skilled in the art. It is assumed that thoseskilled in the art are familiar with the general architecture of datastorage system, and in particular operations of data storage systems fordata storage and/or operations on stored data. It may be noted that anumbered element is numbered according to the figure in which theelement is introduced, and is typically referred to by that numberthroughout succeeding figures.

FIG. 1 illustrates an architecture 100, in accordance with oneembodiment. As shown in FIG. 1, a plurality of remote networks 102 areprovided including a first remote network 104 and a second remotenetwork 106. A gateway 101 may be coupled between the remote networks102 and a proximate network 108. In the context of the presentarchitecture 100, the networks 104, 106 may each take any formincluding, but not limited to a LAN, a WAN such as the Internet, publicswitched telephone network (PSTN), internal telephone network, etc.

In use, the gateway 101 serves as an entrance point from the remotenetworks 102 to the proximate network 108. As such, the gateway 101 mayfunction as a router, which is capable of directing a given packet ofdata that arrives at the gateway 101, and a switch, which furnishes theactual path in and out of the gateway 101 for a given packet.

Further included is at least one data server 114 coupled to theproximate network 108, and which is accessible from the remote networks102 via the gateway 101. It should be noted that the data server(s) 114may include any type of computing device/groupware. Coupled to each dataserver 114 is a plurality of user devices 116. User devices 116 may alsobe connected directly through one of the networks 104, 106, 108. Suchuser devices 116 may include a desktop computer, lap-top computer,hand-held computer, printer, smartphone, or any other type of logicdevice. It should be noted that a user device 111 may also be directlycoupled to any of the networks, in one embodiment.

A peripheral 120 or series of peripherals 120, e.g., facsimile machines,printers, networked and/or local storage units or systems, etc., may becoupled to one or more of the networks 104, 106, 108. It should be notedthat databases and/or additional components may be utilized with, orintegrated into, any type of network element coupled to the networks104, 106, 108. In the context of the present description, a networkelement may refer to any component of a network.

According to some approaches, methods and systems described herein maybe implemented with and/or on virtual systems and/or systems whichemulate one or more other systems, such as a UNIX system which emulatesan IBM z/OS environment, a UNIX system which virtually hosts a MICROSOFTWINDOWS environment, a MICROSOFT WINDOWS system which emulates an IBMz/OS environment, etc. This virtualization and/or emulation may beenhanced through the use of VMWARE software, in some embodiments.

In more approaches, one or more networks 104, 106, 108, may represent acluster of systems commonly referred to as a “cloud.” In cloudcomputing, shared resources, such as processing power, peripherals,software, data, servers, etc., are provided to any system in the cloudin an on-demand relationship, thereby allowing access and distributionof services across many computing systems. Cloud computing typicallyinvolves an Internet connection between the systems operating in thecloud, but other techniques of connecting the systems may also be used.

FIG. 2 shows a representative hardware environment associated with auser device 116 and/or server 114 of FIG. 1, in accordance with oneembodiment. Such figure illustrates a typical hardware configuration ofa workstation having a central processing unit 210, such as amicroprocessor, and a number of other units interconnected via a systembus 212.

The workstation shown in FIG. 2 includes a Random Access Memory (RAM)214, Read Only Memory (ROM) 216, an I/O adapter 218 for connectingperipheral devices such as disk storage units 220 to the bus 212, a userinterface adapter 222 for connecting a keyboard 224, a mouse 226, aspeaker 228, a microphone 232, and/or other user interface devices suchas a touch screen and a digital camera (not shown) to the bus 212,communication adapter 234 for connecting the workstation to acommunication network 235 (e.g., a data processing network) and adisplay adapter 236 for connecting the bus 212 to a display device 238.

The workstation may have resident thereon an operating system such asMicrosoft Windows® Operating System (OS), MAC OS, UNIX OS, etc. It willbe appreciated that a preferred embodiment may also be implemented onplatforms and operating systems other than those mentioned. A preferredembodiment may be written using XML, C, and/or C++ language, or otherprogramming languages, along with an object oriented programmingmethodology. Object oriented programming (OOP), which has becomeincreasingly used to develop complex applications, may be used.

Referring now to FIG. 3, there is illustrated an example block diagramof an information management system 300 that includes a set of networkeddata storage systems 320 a, 320 b . . . 320 n, client devices 330 a, 330b . . . 330 n, and a metadata management system 302 in communication viaa data network 310 and in accordance with implementations of thisdisclosure. It can be appreciated that the implementations disclosedherein are not limited by the number of storage devices or data storagesystems attached to data network 310. It can be further appreciated thatstorage devices or data storage systems attached to data network 310 arenot limited by communication protocols, storage environment, physicallocation, etc.

In one embodiment, each data storage system 320 a, 320 b . . . 320 n mayinclude a storage subsystem 321 and storage devices 322. The storagesubsystem 321 may comprise a storage server or an enterprise storageserver, such as the IBMS Enterprise Storage Server®. (IBM and EnterpriseStorage Server are registered trademarks of IBM). The storage devices322 may comprise storage systems known in the art, such as a DirectAccess Storage Device (DASD), Just a Bunch of Disks (JBOD), a RedundantArray of Independent Disks (RAID), a virtualization device, tapestorage, optical disk storage, or any other storage system known in theart. The storage devices 322 may comprise content organized as objectstorage, file storage, and/or block storage. In certain embodiments,multiple storage subsystems may be implemented in one storage subsystem321 and storage devices 322, or one storage subsystem may be implementedwith one or more storage subsystems having attached storage devices.

One or more of the data storage systems 320 a, 320 b . . . 320 n may bebackup storage systems and/or may include back storage locations. Abackup storage system may include, in addition to storage subsystemsand/or storage devices, for example, backup server software, which mayinclude a communication component, a storage manager component, adatabase component, or the like. The database component may keep arecord of all of the backups and restores that have occurred. As anexample, IBM® TIVOLI® Storage Manager may be used for the backup datastorage system. As another example, the backup data storage system maybe an IBM model P690 server, or any other suitable computing device.

In certain embodiments, client devices 330 a, 330 b . . . 330 n may begeneral purpose computers having a plurality of components. Thesecomponents may include a central processing unit (CPU), main memory, I/Odevices, and storage devices (for example, flash memory, hard drives andothers). The main memory may be coupled to the CPU via a system bus or alocal memory bus. The main memory may be used to provide the CPU accessto data and/or program information that is stored in main memory atexecution time. Typically, the main memory is composed of random accessmemory (RAM) circuits. A computer system with the CPU and main memory isoften referred to as a host system. The client devices 330 a, 330 b . .. 330 n can have at least one operating system (e.g., Microsoft Windows,Mac OS X, iOS, IBM z/OS, Linux, other Unix-based operating systems,etc.) installed thereon, which may support or host one or more filesystems and other applications.

The data storage systems 320 a, 320 b . . . 320 n and client devices 330a, 330 b . . . 330 n communicate according to well-known protocols, suchas the Network File System (NFS) or the Common Internet File System(CIFS) protocols, to make content stored on data storage systems 320 a,320 b . . . 320 n appear to users and/or application programs as thoughthe content were stored locally on the client systems 330 a, 330 b . . .330 n. In a typical mode of operation, the client devices 330 a, 330 b .. . 330 n transmit one or more input/output commands, such as an NFS orCIFS request, over the computer network 310 to the data storage systems320 a, 320 b . . . 320 n, which in turn issues an NFS or CIFS responsecontaining the requested content over the network 310 to the respectiveclient devices 330 a, 330 b . . . 330 n.

The client devices 330 a, 330 b . . . 330 n may execute (internallyand/or externally) one or more applications, which generate andmanipulate the content on the one or more data storage systems 320 a,320 b . . . 320 n. The applications generally facilitate the operationsof an organization (or multiple affiliated organizations), and caninclude, without limitation, mail server applications (e.g., MicrosoftExchange Server), file server applications, mail client applications(e.g., Microsoft Exchange Client), database applications (e.g., SQL,Oracle, SAP, Lotus Notes Database), word processing applications (e.g.,Microsoft Word), spreadsheet applications, financial applications,presentation applications, browser applications, mobile applications,entertainment applications, and so on. The applications may also havethe ability to access (e.g., read and write to) data storage systems 320a, 320 b . . . 320 n using a network file system protocol such as NFS orCIFS.

As shown, the data storage systems 320 a, 320 b . . . 320 n, the clientdevices 330 a, 330 b . . . 330 n, the metadata management system 302,and other components in the information management system 300 can beconnected to one another via a communication network 310. Thecommunication network 310 can include one or more networks or otherconnection types including any of following, without limitation: theInternet, a wide area network (WAN), a local area network (LAN), aStorage Area Network (SAN), a Fibre Channel connection, a Small ComputerSystem Interface (SCSI) connection, a virtual private network (VPN), atoken ring or TCP/IP based network, an intranet network, apoint-to-point link, a cellular network, a wireless data transmissionsystem, a two-way cable system, an interactive kiosk network, asatellite network, a broadband network, a baseband network, a neuralnetwork, a mesh network, an ad hoc network, other appropriate wired,wireless, or partially wired/wireless computer or telecommunicationsnetworks, combinations of the same or the like. The communicationnetwork 310 in some cases may also include application programminginterfaces (APIs) including, e.g., cloud service provider APIs, virtualmachine management APIs, and hosted service provider APIs.

In an embodiment, the metadata management system 302 is configured tocollect metadata corresponding to contents of the storage systems 320 a,320 b . . . 320 n, and generate and store information relating tocharacteristics of the stored data and/or metadata. The metadatamanagement system 302 can be present to, for example, store, organize,protect, manage, manipulate, move, analyze, and/or process metadata ofdata storage systems 320 a, 320 b . . . 320 n. Specifically, themetadata management system 302 may also be configured to generate andstore other types of information that generally provides insights intothe contents of the storage systems 320 a, 320 b . . . 320 n. Themetadata management system 302 can provide a number of benefitsincluding improved storage operations, faster data operationperformances, enhanced scalability, or the like. As one specific examplewhich will be discussed below in further detail, the metadata managementsystem 302 can act as a cache for storing metadata, for analyzingmetadata, adding metadata tags, updating metadata tags, or the like.

In certain embodiments, the metadata management system 302 includes ametadata collection system 351 in communication with a metadata store352 and a classifier 353.

Generally speaking, the metadata management system 302 may beimplemented as a storage system (some combination of hardware andsoftware) that manages, coordinates, and facilitates the transmission ofmetadata between a client computing device and one or more data storagesystems, and/or between the one or more storage systems such thatoperations related to the metadata management system 302 do notsignificantly impact performance of other components in the informationmanagement system 300. Moreover, as will be described further, themetadata management system 302 may be configured to make calls to datastorage system 320 a, 320 b . . . 320 n and/or receive information fromthe data storage system 320 a, 320 b . . . 320 n through data network310. For example, metadata management system 302 may provide API calls,commands, or other services allowing for the storage, management, andretrieval of metadata, system data blocks, or items. In one embodiment,metadata management system 302 may include or be associated with one ormore storage devices, providers, or solutions for the storage of items,system data blocks, or other data.

In an embodiment, the metadata collection system 351 may collect themetadata from data storage systems 320 a, 320 b . . . 320 n and store itin the metadata store 352. The metadata collected by the metadatacollection system 351 may be system metadata, event metadata, scanmetadata, or any other type of metadata. System metadata includesmetadata collected and stored by the data storage systems 320 a, 320 b .. . 320 n internally using any now or hereafter known methods. Eventmetadata includes metadata corresponding to an event (or data operation)executed on the data storage systems 320 a, 320 b . . . 320 n and mayinclude, without limitation, information about the data set relating tothe event (e.g., file name, location, author, size, or the like);information about the event (e.g., event type, function performed,resulting changes to the data, time of event, or the like); informationabout the application or client device that performed and/or initiatedthe event; information about the data storage system on which the eventwas executed, and/or the like. Scan metadata includes metadata collectedby the metadata collection system 351 by externally scanning thecontents (e.g., documents, files, objects, images, etc.) of the datastorage systems 320 a, 320 b . . . 320 n. System metadata and scanmetadata may include, without limitation content metadata that providesinformation on data objects stored in data storage systems 320 a, 320 b. . . 320 n; volume metadata that provides information on volumesconfigured in the data storage systems 320 a, 320 b . . . 320 n in whichthe content is stored; device class metadata that defines the type ofstorage hardware used for a particular storage pool (the device classmay indicate the storage device type and specifies a device type andmedia management information, such as recording format, estimatedcapacity, and labeling prefixes); library metadata that provides afurther level of abstraction representing a storage entity that containsa media changer in addition to drives and tapes for storing data; and/orthe like.

In certain embodiments, each of the data storage systems 320 a, 320 b .. . 320 n may collect and store the system metadata corresponding tocontents of the respective data storage systems internally using any nowor hereafter known methods. For example, the data storage systems 320 a,320 b . . . 320 n may collect metadata when the contents are created,modified, and/or periodically using any now or hereafter known methods.The data storage systems 320 a, 320 b . . . 320 n may transmit thecollected system metadata to the metadata collection system 351 (via,for example, an API). The internally collected system metadata may betemporarily and/or permanently stored on the data storage systems 320 a,320 b . . . 320 n. For example, FIG. 3 illustrates system metadata 321a, 321 b . . . 321 n stored in data storage systems 320 a, 320 b . . .320 n, respectively.

Alternatively and/or additionally, the metadata collection system 351may collect the metadata by performing a periodic scan of one or more ofthe data storage systems 320 a, 320 b . . . 320 n (“scan metadata”). Themetadata collection system may utilize any now or hereafter knowntechniques to collect the metadata from the data storage systems 320 a,320 b . . . 320 n (described below). For example, one approach togathering stored data metadata is by scanning a data storage system fromoutside using standard client access network protocols such as NFS andCIFS protocols in a NAS file storage context and SCSI in a block storagecontext. Although not limited to a specific format, the aforementionedmetadata can be of a data format referred to as inode. Alternatively,they may be in a data format referred to as NTFS in Windows® OSs.Metadata used in MacOS® may also be used. In certain embodiments, themetadata collection system 330 may use deep learning, machine learning,and/or other methods to parse the contents of the data storage systems320 a, 320 b . . . 320 n and collect the metadata (e.g., using the IBMWatson™ QA system available from International Business MachinesCorporation, or other natural language processing and/or deep learningsystems).

Alternatively and/or additionally, the metadata collection system 351may collect event metadata. In certain embodiments, the data storagesystems 320 a, 320 b . . . 320 n may forward event metadata to themetadata collection system 351 upon occurrence of one or more events(e.g., a copy operation, backup operation, an encryption operation, orthe like). For example, the metadata collection system 351 may installan event monitoring agent on the data storage systems 320 a, 320 b . . .320 n. In one or more embodiments, the metadata collection system 351may configure the data storage systems 320 a, 320 b . . . 320 n to sendan event notification along with event metadata every time an eventoccurs for data residing on a data storage system. Alternatively, themetadata collection system 351 may configure the data storage systems320 a, 320 b . . . 320 n to send a collection of event notificationand/or event metadata periodically.

The metadata collection system 351 may receive streams of log data(e.g., data storage system logs, client device logs) from many sources,convert log entries from the log data into events, and store the eventsin metadata store 352 based on fields specified in source typedefinitions (also referred to herein simply as source types). Each eventrepresents a particular log entry. The events that are stored in themetadata store 352 may be based on log entries from various sources andmay have different formats. Examples of log entries include simplenetwork management protocol (SNMP) logs, reports from devices and/orapplications running on devices, application programming interface (API)call records, information exchange protocols, remote authenticationdial-in user service (RADIUS) logs, lightweight directory accessprotocol (LDAP) logs, security assertion markup language (SAML)messages, and so forth. These diverse events may all be stored andindexed in the metadata store 352, which may be a non-homogenousdatabase, in a manner that enables the events to be searched and linkedtogether.

For example, an event monitor agent may include a filter driver programand may be deployed on an input/output port of the data storage system,a read/write port, or data stack and operate in conjunction with a filemanagement program to record events executed on a data storage system.Such operation may involve creating a data structure such as a record orjournal of each event. The records may be stored in a journal datastructure and may chronicle events in any form or structure (e.g., on aninteraction by interaction basis). The journal data structure mayinclude information regarding the type of event that has executed alongwith certain configurable relevant properties of the data involved inthe event. One example of such a monitor program may include Microsoft'sChange Journal. Each data storage system may then transmit the event logperiodically and/or every time an event occurs to the data storagesystem. Alternatively and/or additionally, the metadata collectionsystem 351 may periodically consult the recorded interactions for newentries. If new entries exist, the metadata collection system 351 mayexamine the entries, and if deemed relevant, the entries may beanalyzed, parsed, and written to the metadata store 352 as an update.

In some other embodiments, the metadata collection system 351 may alsomonitor data interactions between the data storage systems 320 a, 320 b. . . 320 n and/or between the client devices 330 a, 330 b . . . 330 nand the data storage systems 320 a, 320 b . . . 320 n, using anysuitable monitoring methods, to collect event metadata. For example, themetadata collection system 351 may monitor data interactions bymonitoring file system managers associated with each of the data storagesystems 320 a, 320 b . . . 320 n (e.g., operating system programs, aFAT, an NTFS, or the like that may be used to manage data movement toand/or from a mass storage device). In another example, the metadatacollection system 351 may monitor data interactions by monitoring thenetwork traffic on the communication network 310 using any now orhereafter known methods. In yet another example, the metadata collectionsystem 351 may collect event metadata by interfacing and/orcommunicating with a virtual file system (VFS) layer that transfers dataoperation requests between the client devices 330 a, 330 b . . . 330 nand the data storage systems 320 a, 320 b . . . 320 n.

In certain embodiments, the metadata collection system 351 may collectsystem metadata, scan metadata, and/or event metadata in a manner thatduplication of the collected metadata may be minimized. For example, themetadata collection system 351 may analyze the system metadata and mayonly collect scan metadata for metadata that is not included in the scanmetadata and/or to update the system metadata periodically. Similarly,upon initialization the metadata collection system 351 may first collectsystem metadata and/or scan metadata from the data storage systems 320a, 320 b . . . 320 n (using one or more methods described above) beforestarting collection of event metadata. This may be done in order toobtain an accurate picture of the data being scanned and/or to maintainreferential integrity within the system.

Duplication may also be prevented by saving only the latest metadatacorresponding to a data set. For example, if an event corresponding to apreviously scanned data set is registered, the metadata collectionsystem may overwrite the previously stored scan metadata for that dataset with the new event metadata (or vice versa).

Events according to certain embodiments are generally data operationsexecuted on the one or more data storage systems such as, withoutlimitation, data migration operations (e.g., copy, backup, archive,email etc.), writing new data on the data storage system, reading datafrom the data storage system, deletion of data, changing one or moreproperties of data and/or associated metadata (e.g., rename, accesspermissions, security, encryption, or the like), printing, or othertypes of data operations. Such data operations lead to a modification inexisting metadata of the corresponding data set and/or creation of newmetadata (e.g., when new data set is created). Event metadata,therefore, may also include information relating to changes in themetadata corresponding to a data set.

For example, operations may be available to interact with stored data,including open, write new file (e.g., PUT), write (append to data set),write (modify an existing data set), close, read (e.g., GET), SAVE,RENAME, DELETE, or the like. A PUT operation writes a new object to astorage device of a data storage system or creates an updated version ofan existing object on a storage device, and in the latter instance, theprevious version may or may not be removed from the storage device.Typically, however, when an updated version of an existing object iswritten to the memory device, the newer version is identified inmetadata as an update (e.g., “version 2)”, while older versions (e.g.,“version 1”) remain stored on the storage device. A DELETE operation istypically associated with writing a new version of an object to astorage device (e.g., via a PUT operation) and indicating a deletion ofthe old version. Where the old version of the object is physicallyremoved from a storage device, the removal may be hard (e.g., the oldversion of the object is immediately rewritten as zero-byte version) orsoft (e.g., the old version of the object is marked deleted in metadataand later rewritten). In one example of removal, the old version of theobject may be cleaned up by an out-of-band process. A GET operationretrieves a representation of an object already stored on a storagedevice, for instance, in order to read the object.

As discussed above, events may also include data migration operationsthat involve the copying or migration of data between differentlocations in the information management system 300 in an original/nativeand/or one or more different formats. For example, events can includeoperations in which stored data is copied, migrated, or otherwisetransferred from one or more first storage systems to one or more secondstorage systems, from one or more first storage systems to one or moreclient devices, and/or within a storage system. Such operations caninclude by way of example, backup operations, archive operations,information lifecycle management operations such as hierarchical storagemanagement operations, replication operations (e.g., continuous datareplication operations), snapshot operations, deduplication orsingle-instancing operations, auxiliary copy operations, and the like.As will be discussed, some of these operations involve the copying,migration or other movement of data, without actually creating multiple,distinct copies of metadata in the data storage systems itself.Nonetheless, some or all of these operations are referred to as “copy”operations for simplicity.

Backup Operations: A backup operation creates a copy of a version ofdata (e.g., one or more files or other data units) in a data storagesystem at a particular point in time. Each subsequent backup copy may bemaintained independently of the first. Further, a backup copy in someembodiments is generally stored in a form that is different than thenative format, e.g., a backup format. This can be in contrast to theversion in the corresponding data storage system from which the backupcopy is derived, and which may instead be stored in a native format ofthe source application(s). In various cases, backup copies can be storedin a format in which the data is compressed, encrypted, deduplicated,and/or otherwise modified from the original application format. Forexample, a backup copy may be stored in a backup format that facilitatescompression and/or efficient long-term storage.

Backup copies can have relatively long retention periods as compared tocorresponding data (“primary data”), and may be stored on media withslower retrieval times than primary data and certain other types ofsecondary copies. On the other hand, backups may have relatively shorterretention periods than some other types of secondary copies such asarchive copies (described below). Backups may sometimes be stored at onoffsite location. Backup operations can include full, synthetic orincremental backups. A full backup in some embodiments is generally acomplete image of the data to be protected. However, because full backupcopies can consume a relatively large amount of storage, it can beuseful to use a full backup copy as a baseline and only store changesrelative to the full backup copy for subsequent backup copies.

For instance, a differential backup operation (or cumulative incrementalbackup operation) tracks and stores changes that have occurred since thelast full backup. Differential backups can grow quickly in size, but canprovide relatively efficient restore times because a restore can becompleted in some cases using only the full backup copy and the latestdifferential copy.

An incremental backup operation generally tracks and stores changessince the most recent backup copy of any type, which can greatly reducestorage utilization. In some cases, however, restore times can berelatively long in comparison to full or differential backups becausecompleting a restore operation may involve accessing a full backup inaddition to multiple incremental backups.

Any of the above types of backup operations can be at the volume-level,file-level, or block-level. Volume level backup operations generallyinvolve the copying of a data volume (e.g., a logical disk or partition)as a whole. In a file-level backup, the information management system300 may generally track changes to individual files at the file-level,and includes copies of files in the backup copy. In the case of ablock-level backup, files are broken into constituent blocks, andchanges are tracked at the block-level. Upon restore, the informationmanagement system 300 reassembles the blocks into files in a transparentfashion.

Far less data may actually be transferred and copied to secondarystorage devices during a file-level copy than a volume-level copy.Likewise, a block-level copy may involve the transfer of less data thana file-level copy, resulting in faster execution times. However,restoring a relatively higher-granularity copy can result in longerrestore times. For instance, when restoring a block-level copy, theprocess of locating constituent blocks can sometimes result in longerrestore times as compared to file-level backups Similar to backupoperations, the other types of secondary copy operations describedherein can also be implemented at either the volume-level, file-level,or block-level.

Archive Operations: Because backup operations generally involvemaintaining a version of the copied data in primary data and alsomaintaining backup copies in secondary storage device(s), they canconsume significant storage capacity. To help reduce storageconsumption, an archive operation according to certain embodimentscreates a secondary copy by both copying and removing source data. Or,seen another way, archive operations can involve moving some or all ofthe source data to the archive destination. Thus, data satisfyingcriteria for removal (e.g., data of a threshold age or size) from thesource copy may be removed from source storage. Archive copies aresometimes stored in an archive format or other non-native applicationformat. The source data may be primary data or a secondary copy,depending on the situation. As with backup copies, archive copies can bestored in a format in which the data is compressed, encrypted,deduplicated, and/or otherwise modified from the original applicationformat. In addition, archive copies may be retained for relatively longperiods of time (e.g., years) and, in some cases, are never deleted.Archive copies are generally retained for longer periods of time thanbackup copies, for example. In certain embodiments, archive copies maybe made and kept for extended periods in order to meet complianceregulations.

Moreover, when primary data is archived, in some cases the archivedprimary data or a portion thereof is deleted when creating the archivecopy. Thus, archiving can serve the purpose of freeing up space in theprimary storage device(s) Similarly, when a secondary copy is archived,the secondary copy may be deleted, and an archive copy can thereforeserve the purpose of freeing up space in secondary storage device(s). Incontrast, source copies often remain intact when creating backup copies.

Snapshot Operations: Snapshot operations can provide a relativelylightweight, efficient mechanism for protecting data. From an end-userviewpoint, a snapshot may be thought of as an “instant” image of theprimary data at a given point in time, and may include state and/orstatus information relative to an application that creates/manages thedata. In one embodiment, a snapshot may generally capture the directorystructure of an object in primary data such as a file or volume or otherdata set at a particular moment in time and may also preserve fileattributes and contents. A snapshot in some cases is created relativelyquickly, e.g., substantially instantly, using a minimum amount of filespace, but may still function as a conventional file system backup.

A “hardware snapshot” (or “hardware-based snapshot”) operation can be asnapshot operation where a target storage device (e.g., a primarystorage device or a secondary storage device) performs the snapshotoperation in a self-contained fashion, substantially independently,using hardware, firmware and/or software residing on the storage deviceitself. For instance, the storage device may be capable of performingsnapshot operations upon request, generally without intervention oroversight from any of the other components in the information managementsystem 300. In this manner, hardware snapshots can off-load othercomponents of information management system 300 from processing involvedin snapshot creation and management.

A “software snapshot” (or “software-based snapshot”) operation, on theother hand, can be a snapshot operation in which one or more othercomponents in information management system 300 implement a softwarelayer that manages the snapshot operation via interaction with thetarget storage device. For instance, the component implementing thesnapshot management software layer may derive a set of pointers and/ordata that represents the snapshot. The snapshot management softwarelayer may then transmit the same to the target storage device, alongwith appropriate instructions for writing the snapshot.

Some types of snapshots do not actually create another physical copy ofall the data as it existed at the particular point in time, but maysimply create pointers that are able to map files and directories tospecific memory locations (e.g., to specific disk blocks) where the dataresides, as it existed at the particular point in time. For example, asnapshot copy may include a set of pointers derived from the file systemor an application. In some other cases, the snapshot may be created atthe block-level, such as where creation of the snapshot occurs withoutawareness of the file system. Each pointer points to a respective storeddata block, so that collectively, the set of pointers reflect thestorage location and state of the data block (e.g., file(s) or volume(s)or data set(s)) at a particular point in time when the snapshot copy wascreated.

Once a snapshot has been taken, subsequent changes to the file systemtypically do not overwrite the blocks in use at the time of thesnapshot. Therefore, the initial snapshot may use only a small amount ofdisk space needed to record a mapping or other data structurerepresenting or otherwise tracking the blocks that correspond to thecurrent state of the file system. Additional disk space is usuallyrequired only when files and directories are actually later modified.Furthermore, when files are modified, typically only the pointers whichmap to blocks are copied, not the blocks themselves. In someembodiments, for example in the case of “copy-on-write” snapshots, whena block changes in primary storage, the block is copied to secondarystorage or cached in primary storage before the block is overwritten inprimary storage, and the pointer to that block changed to reflect thenew location of that block. The snapshot mapping of file system data mayalso be updated to reflect the changed block(s) at that particular pointin time. In some other cases, a snapshot includes a full physical copyof all or substantially all of the data represented by the snapshot.

A snapshot copy in many cases can be made quickly and withoutsignificantly impacting primary computing resources because largeamounts of data are not copied or moved. In some embodiments, a snapshotmay exist as a virtual file system, parallel to the actual file system.Users in some cases gain read-only access to the record of files anddirectories of the snapshot. By electing to restore primary data from asnapshot taken at a given point in time, users may also return thecurrent file system to the state of the file system that existed whenthe snapshot was taken.

Replication Operations: Another type of secondary copy operation is areplication operation. Some types of secondary copies are used toperiodically capture images of primary data at particular points in time(e.g., backups, archives, and snapshots). However, it can also be usefulfor recovery purposes to protect primary data in a more continuousfashion, by replicating the primary data substantially as changes occur.In some cases a replication copy can be a mirror copy, for instance,where changes made to primary data are mirrored or substantiallyimmediately copied to another location (e.g., to secondary storagedevice(s)). By copying each write operation to the replication copy, twostorage systems are kept synchronized or substantially synchronized sothat they are virtually identical at approximately the same time. Whereentire disk volumes are mirrored, however, mirroring can requiresignificant amount of storage space and utilizes a large amount ofprocessing resources.

Deduplication/Single-Instancing Operations: Another type of datamovement operation is deduplication or single-instance storage, which isuseful to reduce the amount of data within the system. For instance,some or all of the above-described secondary storage operations caninvolve deduplication in some fashion. New data is read, broken downinto portions (e.g., sub-file level blocks, files, etc.) of a selectedgranularity, compared with blocks that are already stored, and only thenew blocks are stored. Blocks that already exist are represented aspointers to the already stored data.

Information Lifecycle Management and Hierarchical Storage ManagementOperations: In some embodiments, files and other data over theirlifetime move from more expensive, quick access storage to lessexpensive, slower access storage. Operations associated with moving datathrough various tiers of storage (e.g., as shown in FIG. 3) aresometimes referred to as information lifecycle management (ILM)operations.

One type of ILM operation is a hierarchical storage management (HSM)operation. A HSM operation is generally an operation for automaticallymoving data between classes of storage devices, such as betweenhigh-cost and low-cost storage devices. For instance, an HSM operationmay involve movement of data from primary storage devices to secondarystorage devices, or between tiers of the same storage devices. With eachtier, the storage devices may be progressively relatively cheaper, haverelatively slower access/restore times, etc. For example, movement ofdata between tiers may occur as data becomes less important over time.

In some embodiments, an HSM operation is similar to an archive operationin that creating an HSM copy may (though not always) involve deletingsome of the source data, e.g., according to one or more criteria relatedto the source data. For example, an HSM copy may include data fromprimary data or a secondary copy that is larger than a given sizethreshold or older than a given age threshold and that is stored in abackup format. Often, and unlike some types of archive copies, HSM datathat is removed or aged from the source copy is replaced by a logicalreference pointer or stub. The stub may also include some metadataassociated with the corresponding data, so that a file system and/orapplication can provide some information about the data block and/or alimited-functionality version (e.g., a preview) of the data block.According to one example, files are generally moved between higher andlower cost storage depending on how often the files are accessed. An HSMcopy may be stored in a format other than the native application format(e.g., where the data is compressed, encrypted, deduplicated, and/orotherwise modified from the original application format). In some cases,copies which involve the removal of data from source storage and themaintenance of stub or other logical reference information on sourcestorage may be referred to generally as “on-line archive copies”. On theother hand, copies which involve the removal of data from source storagewithout the maintenance of stub or other logical reference informationon source storage may be referred to as “off-line archive copies”.

It will be understood to those skilled in the art that it is possible toemploy “event” definitions that may capture a relatively broad or narrowset of data operations executed on a data storage system, allowing auser to customize the metadata collection system 351 to meet certainmetadata collection goals. Such “event” definitions may define ordescribe data movement, changes, manipulations or other operations orinteractions that may be of interest to a system user or administrator(e.g., any operation that “touches” data may be recorded along with theaction or operation that caused the interaction (e.g. read, write, copy,parse, or the like). Moreover, change definitions may evolve over timeor may be dynamic based on the entries sent to the metadata store 352.For example, if expected results are not obtained, change definitionsmay be modified or additional definitions used until appropriate ordesired results are obtained. This may be accomplished, for example byglobally linking certain libraries of “event” definitions andselectively enabling libraries on a rolling basis until acceptableresults are achieved. This process may be performed after the initialactivation of the metadata collection system 351 and periodicallythereafter, depending on changing needs or objectives.

Moreover, in some embodiments, the system may support the use of “userdata tags” that allow certain types of information stored in the datastorage systems 320 a, 320 b . . . 320 n to be tagged so they may beidentified and tracked throughout the system. As such, if a data blockthat includes a user data tag is touched, an event log is recordedand/or sent or collected by the metadata collection system 351. Forexample, a user may designate a particular type of data or informationsuch as project information, or information shared between or accessedby particular group of users to be tracked across the system or throughvarious levels of storage. This may be accomplished through a userinterface that allows a user to define certain information to be tagged,for example, by using any available attribute within the system such asthose specified above with respect to the classification agent or filterused in the system. In some embodiments, the user may define one or moretags using these or other attributes which may be further refined bycombining them through the use of logical or Boolean operators to definea certain tag expression.

For example, a user may define a certain user data tag by specifying oneor more criteria to be satisfied such as certain system users, a certaindata permission level, a certain project, combinations of the same orthe like. These criteria may be defined using logical operators such asAND or OR operators to conditionally combine various attributes tocreate a condition that defines a tag. In certain embodiments,information satisfying that criteria may be tagged and tracked withinthe system. For example, the metadata store 352 may contain entrieskeeping track of entries satisfying the tag criteria along withinformation relating to the types of operations performed on theinformation as well as certain metadata relating to the data content andits location in the data storage systems 320 a, 320 b . . . 320 n. Thisallows the system to search the metadata store 352 at a particular levelof storage for the information, and quickly locate it within massstorage device for potential retrieval.

Referring back to FIG. 3, metadata (system metadata and/or eventmetadata) collected by the metadata collection system 351 is stored inthe metadata store 352 outside of the data storage systems 320 a, 320 b. . . 320 n. In one or more embodiments, the metadata store 352 may beany type of data structure that allows for easy and efficient searchingof the stored metadata. Examples may include, without limitation,relational database storage (e.g., SQL databases), key-value typestorages (e.g., noSQL databases), columnar storages (e.g., parquet), orthe like. The metadata store 352 may also include an index associatedwith each piece of metadata and stored with the metadata. The index maycontain information such as each of the locations where the data setcorresponding to the metadata is located, user access informationdescribing which users are permitted to view the contents of the dataset, type of data structure corresponding to the data set, or the like.The content index may be used to facilitate search and retrieval of adata set corresponding to metadata, such as in response to a userrequest to restore a particular file.

In one or more embodiments, the metadata store 352 may be a datastructure in the form of a NoSQL (“Not-only-Structured-Query-Language”)database. In one embodiment, the metadata store 352 is implemented usinga NoSQL database that uses a key-value store, a document store, and/or awide column store. Specifically, event metadata collected by themetadata collection system 351 may be stored in a NoSQL type databasestructure. A NoSQL database may be, by way of example, Cloudant® ApacheCassandra™, Object Storage, Apache HBase™, Hazelcast®, etc.

A NoSQL database provides a mechanism for storage and retrieval of datathat is modeled in means other than the tabular relations used inrelational databases. Typical motivations for this approach includesimplicity of design, horizontal scaling, and finer control overavailability. NoSQL databases have features of self-organizing,self-managing, low cost, high scalability, high concurrency, simplyquery relation, and so on. To compare a NoSQL database to a relationaldatabase, a form in the relational database usually stores a formatteddata structure, and components of all entry fields are the same. Even ifnot every entry needs all fields, the relational database will allocateall fields to each entry. Such structure can potentially cause aperformance bottleneck in a relational database. On the other hand, aNoSQL database typically carries out storage with a Key/Value pair, andits structure is not fixed. Each entry can have different fields, andeach entry can add some key value pairs of its own according to arequirement so as not to be limited to the fixed structure and tothereby reduce some time and space overheads.

In an embodiment, an “entry” corresponding to an event may be a recordin a NoSQL database, and can also be regarded as a data object instancein the NoSQL database. Each entry can possess a unique identifier (ID),and can comprise zero or more Key/Value pairs. Usage examples includestoring millions of data records as key-value pairs in one or a fewassociative arrays. A key-value pair is a fundamental datarepresentation in computing systems and applications, in which all orpart of the data model may be expressed as a collection of tuples<attribute name, value>, for which each element is a key-value pair. Anassociative array is an unordered list of unique attributes withassociated values. Such organization is particularly useful forstatistical or real-time analysis of growing lists of data elements.According to an embodiment of the present invention, a pre-definedspecificator can be used to distinguish between individual Key/Valuepairs. For example, different Key/Value pairs are distinguished by acomma. Meanwhile, the “key” and the “value” within each Key/Value paircan be separated by a pre-defined delimiter, for example, a colon, thusthe key in a Key/Value pair can be determined from the Key/Value pairaccording to the delimiter. At the same time, the “value” in a Key/Valuepair can be extended by a pre-defined extension symbol, for example,square brackets which can be used to represent that the “value” in aKey/Value pair comprises more than two attributes. Each attribute in themore than two attributes can either be a real “value”, or be a Key/Valuepair in which the “value” can continue to comprise one or moreattribute.

For example, metadata (event metadata, scan metadata and systemmetadata) may include one or more of the following keys andcorresponding values:

Keys Values Data Storage Identity of the Data Storage System on whichSystem the event was executed Time Stamp Time of event File File Name ofthe data set (or “file”) or which the event was executed Title Title ofthe data set Type Type of content (e.g., document (WORD, PDF,etc.)/image (JPEG, GIFF, etc./video/email/web link/blog/or the like)Size Size of file/size of data corresponding to event execution Time ofcreation File Creation time Owner Creator of file (user name, clientdevice, affiliation, department, or other user information etc.) AuthorEvent executor (user name, client device, affiliation, or other userinformation etc.) Event typeCopy/Read/Write/Modify/Delete/Print/Email/etc. Path Data pathcorresponding to event (e.g., copy {F1} to {F2} Text Text ExcerptsFacets Facets (e.g., social security number, patient name, etc.) TagsKeywords (e.g., financial, personal, sensitive, medical, etc.) and/orpreviously associated tags

It should be noted that the above key values are provided as way ofexample only and should not be considered limiting.

It will be understood to those skilled in the art that while other typesof storing formats are not described here in detail, the metadata may bestored in other formats as well (e.g., SQL, Parquet, etc.).

In certain embodiments, the metadata management system 302 also includesa classifier 353 configured to apply custom tags (interchangeablyreferred to as tags) to the metadata before and/or after insertion bythe metadata collection system 351 into the metadata store 352. Incertain embodiments, the facets (described below) associated with themetadata that are indicative of the actual content in the data storagesystems 320 a, 320 b . . . 320 n that corresponds to the tagged metadataand/or the metadata itself may be used as tags.

In some embodiments, the classifier 353 analyzes characteristics,content, format, etc. of the metadata (and not the data itself) to addtags to the metadata. This provides enhanced search and managementcapabilities for data discovery and other purposes. The custom tags, incertain embodiments, significantly reduce the amount of time required toobtain information by reducing and/or substantially eliminating the needto obtain information directly from the source data. The tags can beused to identify files or other data blocks in the data storage systems320 a, 320 b . . . 320 n having pre-defined content (e.g., user-definedkeywords or phrases, other keywords/phrases that are not defined by auser, etc.), and/or metadata (e.g., email metadata such as “to”, “from”,“cc”, “bcc”, attachment name, received time, etc.).

For example, assume a system administrator desires to identify data setsthat a certain user has interacted with, that contain content includingcertain keywords, content having characteristics, etc. Rather thansearch each file in each directory and/or all the metadata content,which can be a very time consuming process (especially when the datablocks reside on multiple storage devices or the metadata store includeslarge volumes of data in a schema-less database format), the systemadministrator may search the custom tags in the metadata store 352 toidentify metadata that is associated with tags corresponding to theuser, keywords and/or characteristics (by for example, defining aquery), and may then look up data sets associated with that metadata.

Moreover, in certain embodiments, use of the custom tags in a metadatastore 352 where the custom tags do not reside on a data storage systemitself for satisfying data searches or queries may also reduce theinvolvement of network resources in this process, substantially reducingthe processing burden on the host system. For example, as describedabove, if an administrator desires to identify certain data sets,querying the metadata store 352 rather than the file system virtuallyremoves the host system from the query process (e.g., no brute forcescanning of directories and files in the data storage systems isrequired), allowing the host system to continue performing host tasksrather than be occupied with search tasks.

The classifier 353 may apply the tags by analyzing the collectedmetadata (before and/or after insertion into the metadata store 352) andapplying one or more user defined policies. Alternatively and/oradditionally the classifier 353 may apply the tags automatically byanalyzing the collected metadata (before and/or after insertion into themetadata store 352), and applying one or more classification rulesautomatically derived by the classifier 353 (e.g., based on machinelearning, deep learning, text annotators, or the like). In yet anotherembodiment, the classifier 353 may analyze the content in the datastorage systems 320 a, 320 b . . . 320 n, and apply one or moreclassification rules automatically derived by the classifier 353 (e.g.,based on machine learning, deep learning, text annotators, or the like)to tag collected metadata associated with that content. Differentmethods for applying the custom tags are described below in detail.

In an embodiment, where the classifier 353 may apply the tags byanalyzing the metadata and applying one or more user defined policies,the classifier 353 may include and/or may be in communication with apolicy engine 355. The policy engine 355 may include a set of userconfigurable policies. In these examples, a policy is a set ofclassification rules. The policy may also include any other data orparameters in addition to the set of classification rules that can beused to interpret how the metadata tags are to be assigned.

For example, a policy may include multiple rules for classifyingmetadata and/or may specify the tag(s) to be applied if the ruleconditions are satisfied. For example, a user may specify a tag to beassociated with a metadata based on one or more characteristics of themetadata.

In certain embodiments, the set of rules for a tag can be defined in atag definition that is typed directly into a user interface program(e.g., a REST API, SDK, or the like) and written into the policy engine.In an alternative embodiment, the tag definition can be represented in adefinition file. If a definition file is used, it can use the XML markuplanguage or any document structure. In an embodiment, a user may createnew customized tags. Alternatively and/or additionally, the user may bepresented a list of pre-defined tags and the user may choose and/ormodify such tags. The tags may be relevant to a user, task, businessobjective, or the like.

A user may define a classification policy by indicating criteria,parameters or descriptors of the policy via a graphical user interfacethat provides facilities to present information and receive input data,such as a form or page with fields to be filled in, pull-down menus orentries allowing one or more of several options to be selected, buttons,sliders, hypertext links or other known user interface tools forreceiving user input. For example, a user may define tags “confidential”and “access level 2” if the metadata includes certain keywords (e.g.,“confidential,” or “privileged”) and/or are associated particular flags(e.g., in metadata identifying a document or email as personal,confidential, etc.).

A policy defines a particular combination of rules, such as users whohave created, accessed or modified a document or data block; file orapplication types; content or metadata keywords; clients or storagelocations; dates of data creation and/or access; review status or otherstatus within a workflow (e.g., reviewed or un-reviewed); modificationtimes or types of modifications; and/or any other data attributes. Apolicy may also be defined using tags already associated with themetadata. For example, a rule may classify all metadata associated withan already assigned tag (e.g., “project”), and apply a second tag (e.g.,a second tag “inactive” that corresponds to the status of projectX).

The various rules used to define a policy may be combined in anysuitable fashion, for example, via Boolean operators, to define acomplex policy. As an example, an E-discovery policy might define a tag“privileged” that is associated with documents or data blocks that (1)were created or modified by legal department staff (i.e., owner orcreator in the metadata is associated with a defined name), (2) weresent to or received from outside counsel via email, and/or (3) containone of the following keywords: “privileged” or “attorney,” “counsel”, orother terms.

Another type of tag which may be added is an entity tag. An entity tagmay be, for example, any content that matches a defined data maskformat. Examples of entity tags might include, e.g., social securitynumbers (e.g., based on a rule that any numerical content matching theformatting mask XXX-XX-XXXX), credit card numbers (e.g., based on a rulecontent having a 13-16 digit string of numbers), SKU numbers, productnumbers, etc.

Policies may, in certain embodiments, may include one or more of thefollowing classification rules for assigning tags to the metadata:

-   -   i. frequency with which metadata and/or corresponding has been        or is predicted to be used, accessed, or modified;    -   ii. size of metadata;    -   iii. user information that created, accessed, modified, or        otherwise utilized content corresponding to the metadata (e.g.,        owner, creator, author, etc.): based on user name, user        affiliation, user access level, etc.    -   iv. time-related factors (e.g., aging information such as time        since the creation or modification of a metadata);    -   v. the identity of applications, client devices and/or other        computing devices that created, accessed, modified, or otherwise        utilized content corresponding to the metadata;    -   vi. a relative sensitivity (e.g., confidentiality) of a data        block, e.g., as determined by its content and/or metadata;    -   vii. the current or historical storage capacity of various        storage devices;    -   viii. the current or historical network capacity of network        pathways connecting various components within the storage        operation cell;    -   ix. access control lists or other security information;    -   x. already existing tags associated with the metadata; and/or    -   xi. the content of metadata (e.g., keywords, tags, etc.).

In an embodiment, the policies defined by the user may also be in akey-value form (if metadata is stored in a noSQL database format), andthe classifier 353 may search for the key-value in the metadata forapplication of a tag defined by the policy. In certain embodiments, theuser may be prompted to define keys that are used by the metadata store352 for sorting and storing the metadata. Optionally, a user may defineany key, and the classifier 353 may apply the tag if a metadata entryincludes either an exact match for the key-value included in the policyand/or if a key-value is similar to the key-value included in thepolicy. For example, if the policy includes a key-value “owner:bob” inthe policy, the classifier may apply the tag if a metadata entryincludes either an exact match for the key-value (i.e., owner:bob)included in the policy and/or a similar key-value (e.g., creator:bob;author:bob; etc.). In one or more embodiments, tagging guidance may beprovided to a user for which the accuracy may increase with use, as thehistory of the user select tags builds up (e.g., using a feedback loopthat provides user with guidance relating to most common keys in themetadata store 352).

For example, consider a user is creating documents for differententerprises having names “\usr/documents/IBM/”;/usr/documents/Company2/”; “/usr/documents/Company3/” etc. in a datastorage system. The metadata collection system will receive eventmetadata corresponding to the creation of the documents that willinclude the document names, formats, sizes, date of creation, etc. Theclassifier 353 may add company name tags to the event metadata based ona user defined policy including rules for classifying metadata based oncompany name (by extracting the company name from the file namesincluded in the event metadata). Another tag describing the documentformat may also be added (e.g., docx, pdf, etc.).

In certain embodiments, tagging based on user-defined policies may beperformed for event metadata upon receipt of event metadata for eachevent by the metadata collection system 351 (e.g., on a first-in,first-out flow) before insertion into the metadata store 352.Alternatively and/or additionally, tagging based on user-definedpolicies may be performed for event metadata periodically; or uponoccurrence of certain conditions (e.g., receipt of user instructions)before (e.g., in a queue) and/or after insertion into the metadata store352. Similarly, tagging based on user-defined policies may be performedfor scan metadata upon receipt of scan metadata every time a scan isperformed by the metadata collection system 351 (e.g., on a first-in,first-out flow) before insertion into the metadata store 352.Alternatively and/or additionally, tagging based on user-definedpolicies may be performed for scan metadata periodically; or uponoccurrence of certain conditions (e.g., receipt of user instructions)before (e.g., in a queue) and/or after insertion into the metadata store352.

In certain embodiments, the classifier 353 may apply the tagsautomatically by analyzing the metadata collected by the metadatacollection engine 351 before and/or after insertion into the metadatastore 352, and applying one or more classification rules automaticallyderived by the classifier 353. It should be noted that data included incertain components of metadata contain meaningful information that isindicative of important facets of the contents corresponding to themetadata, and which can be extracted and analyzed without extractinginformation from the contents itself (e.g., files and objects). Forexample, file system path information, file name, objectbucket/container information, object name, owner information, eventinformation, or the like included in the metadata may includeinformation about important facets of the contents corresponding to themetadata and/or the metadata itself. A facet may comprise a specifictype of information about content to be determined from metadatacomponents and may include words, phrases, or other data descriptorsidentifying unique features of a document/data/content/metadata/etc. Forinstance, a facet may comprise a characteristic of metadata or contenttype determined by text analytics (e.g., sensitive, privileged, or thelike). Other examples of such facets may include, without limitation,organization names, content type, location, user information, or thelike. Facets may be words, phrases, or other data extracted directlyfrom the metadata (e.g., owner name, entity name, document type, etc.)and/or words, phrases, or other descriptors derived based on informationincluded in the metadata.

In an embodiment, the classifier 353 may include a data miner 356 (e.g.,text miner, image miner, audio miner, video miner, and/or the like) toapply data analytics to the components of the metadata to determinefacets associated with the content corresponding to the metadata. Dataanalytics provides techniques to convert textual, audio, video or speechdata into structured data by extracting information e.g., person names,addresses, etc. and classifying content into categories based on thedata and content (i.e., facets).

The data miner 356 may comply with the Unstructured InformationManagement Architecture (UIMA), and include such annotators as alanguage identification annotator to identify the language of metadata;a linguistic analysis annotator to apply linguistic analysis to themetadata; a dictionary lookup annotator to match words and synonyms froma dictionary with words in the content of the metadata and to associatekeywords with user-defined facets; a named entity recognition annotatorto extract person names, locations, and company names; or the like. Itshould be understood that any text analytic technologies similar to UIMAmay be employed to accomplish the techniques described herein. Forexample, other off-the-shelf analytics applications or custom softwareand/or hardware may be used instead of, or in addition to, UIMA.

As is known to those skilled in the art, UIMA developed by IBMCorporation (Armonk, N.Y.) is an open platform for creating, integratingand deploying unstructured information management solutions fromcombinations of semantic analysis and search components to discoverpatterns. It allows easy authoring of annotators, such as the expressionof the format of telephone numbers, or dates, or meeting rooms. Then,given a set of text documents, the UIMA tool applies the variousannotators authored, thereby automatically annotating segments of textby different annotations as authored. IBM product platforms that exposethe UIMA interfaces include the OmniFind Enterprise Edition andAnalytics Edition. The former features UIMA for building full-text andsemantic search indexes, and the latter deploys UIMA for informationextraction and text analysis. The annotators may be driven off of entityspotting, using Information Extraction (IE) techniques, and/or usingnatural language mining (NLM) techniques.

For example, in certain embodiments, the classifier 353 maypre-determine categories such as “sensitive”, “enterprise”, “medicalcontent”, or the like. Further, each category may be associated with aparticular annotator in the data miner 356. An annotator can be anycombination of dictionaries, parsing rules, character rules, languageidentification, semantic analysis, and the like. For example, a “MedicalContent” category may include dictionaries for topics such as surgery,patient, physician, medicine, medical, and the like. In certainembodiments, custom dictionaries may also be leveraged to classify data.For example, a custom dictionary that contains terms found in the codedevelopment environment may be used and may include terms such as ISO,src, software development project names, acronyms associated withdevelopment environments (e.g., PMR, etc.). Annotators may also bedefined that derive relationships in between terms as well.

It should be noted that dictionary entries may be in the form of a noun,verb, list of causes, and a causation time frame. The dictionary mayalso contain other data such as parts of speech (e.g., adjectives,adverbs, etc.), phrases, etc. The dictionary may also contain morecomplex grammar-like constructs. For example, the dictionary may containnoun alternatives and plurals, verb conjugations, and conjunctions orother Boolean terms (e.g., not, or, and, and exclusive-or). Thedictionary may be in any format (e.g., plain text, relational databasetables, nested XML code, etc.). Any number of dictionaries may be usedfor analysis. Hence, the system enables the use of dictionary basedannotators, entity based annotators or extraction (e.g., organizations,people, location, etc.), in addition to the ability to create customannotators based on linguistic nuances and terms of specific industriesas part of the classification process.

In certain embodiments, the facets extracted may be used to tag themetadata and/or identify a different tag, which may be used forretrieval of content corresponding to the data. Alternatively and/oradditionally, facets may be added to a metadata entry in the metadatastore 352 before and/or after insertion into the metadata store 352.

In one or more embodiments, the classifier 353 may also include a rulesengine 357 configured to analyze the facets extracted by the text miner356 to classify the content corresponding to the metadata, and applycustom tags to the metadata (discussed below). Specifically, a tag maybe added based on one or more characteristics of the extracted facets.For example, in the above dictionary examples for code developmentenvironment, if International Organization for Standardization (ISO) andproblem management report (PMR) words are present (extracted facets),the rules engine 357 may tag the metadata with “sensitive” tagindicating that the corresponding content includes sensitive data.Similarly, if the extracted facets include person names that areassociated with an entity, the rules engine 357 may tag the metadatawith “entity name” tag indicating that the corresponding content isassociated with the entity. As such, the classifier 353 may add ‘tags’to entries in the metadata store 352 for identifying various matchedconcepts (based on facet analysis) and/or to map the entries tostandardized resources. In another example, if the extracted facetsinclude medical information and a patient name, the rules engine 357 mayapply tags such as confidential, personal information, etc.

The rules engine 357 may utilize a process of recognizing therelationships, predicates, or dependencies of components of the metadataand/or facets, and thereby extract new, hidden, indirect, or detailedstructural information to classify the extracted metadata and apply atag. For example, the rules engine 357 may include an NLP component thatevaluates the extracted facets and may determine whether the facetsinclude a term from a given dictionary in relationship (e.g.,immediately followed by) with a term from a related dictionary. If themetadata component satisfies that parsing rule, then the NLP componentdetermines a likelihood that content is about the subject matter basedon correlations. If the likelihood exceeds a threshold, the NLPcomponent applies a tag to the event metadata that is indicative of aproperty of the subject matter. For example, if the metadata includes afile system path that belongs to an entity recognized as a law firm, andthe file name includes “non-infringement opinion”; the classifier mayextract facets such as legal document, opinion, client information,etc.; and the rules engine may analyze the facets and theirrelationships to identify tags such as confidential, attorney clientprivileged, work product, legal opinion, or the like. In certainembodiments, the facets may also be used as tags.

While the above disclosure describes using text miners to extract facetsfrom the metadata, other methods such as deep semantic relationshipdetection and analysis. machine learning, or the like are within thescope of this disclosure.

While the above disclosure describes adding tags to the metadata byanalyzing the contents of the metadata itself, the disclosure is not solimiting. In certain embodiments, the system may also analyze specificcontents of the data storage systems 320 a, 320 b . . . 320 n toextracts facets of the contents, and use the facets to add one or moretags to the metadata corresponding to the content in the metadata store352. In an embodiment, the metadata management system 302 may include afacet extraction engine 358 configured to analyze content stored in thedata storage systems 320 a, 320 b . . . 320 n to extract facets. Facetsmay include various dimensions of the content such as, withoutlimitation, users, keywords, time stamps, entity names, document type,or the like. In some embodiments, the extracted facets may be utilizedas tags in the in the metadata store 352. Alternatively and/oradditionally, the rules engine 357 may use the extracted facets toidentify one or more tags to be added to the metadata (as describedabove).

In certain embodiments, the facet extraction engine 358 may extractfacets by analyzing the contents of files, objects, etc. stored in thein the data storage systems 320 a, 320 b . . . 320 n. Alternativelyand/or additionally, the facet extraction engine 358 may extract facetsfrom, for example, metadata associated with the files, objects, filesystems, object containers, storage devices, data storage systems, orthe like, that resides in the data storage systems 320 a, 320 b . . .320 n, For example, the facet extraction engine 358 may extract facetsfrom file headers, object names, etc. that may include metadata (e.g.,size, date of creation, date of modification, author, entity, storagelocation, etc.) about the file or object and its contents. For example,most image file headers store information about image format, size,resolution and color space, and optionally authoring information such aswho made the image, when and where it was made, what camera model andphotographic settings were used (Exif), and so on. Such metadata may beused by the facet extraction engine 358 to extract facets of thecontents of the file or object itself.

In certain embodiments, the contents of the data storage systems 320 a,320 b . . . 320 n that may be analyzed for extracting content facets inresponse to a data operation to be performed on the contents of a datastorage system. For example, if the information system 300 receives arequest to perform a data operation on certain data sets stored in oneor more of the data storage systems 320 a, 320 b . . . 320 n, the systemmay analyze those data sets to extract content facets corresponding tothe data sets before performing the data operation. For example, if auser requests that sensitive content included in a storage subsystem ofdata storage system 320 a must be encrypted, the facet extraction engine358 may analyze content of that storage subsystem, without limitation,entity information, user information, keywords, or the like thatindicate that the content is sensitive. Other data operation requestsmay include, without limitation, backup, archive, deduplication, or thelike.

In one or more embodiments, the facet extraction engine 358 may performfacet extraction using supervised learning, unsupervised learning and/ordeep inspection methods (including, for example, the data mining methodsdescribed above). For example, the facet extraction engine 358 mayutilize the supervised and/or unsupervised learning methods for namedentity extraction and/or classification. Named entity recognition andclassification are important aspects of information extraction toidentify information units such as people, organizations, locationnames, and numeric expressions for time, money and numbers fromunstructured text. Typically, information units or numeric expressionsare first extracted out as named entities from the unstructured text(i.e., named entity recognition), followed by learning a function froman entity to its type, which is selected from predefined categories suchas: People, Organizations, Locations, Products, Genes, Compounds, andTechnologies, etc. (i.e., named entity classification). There areseveral kinds of learning methods depending on the availability oftraining examples. Supervised learning methods infer rules from positiveand negative examples of named entities over a large collection ofannotated documents for each entity type. Supervised learning requires alarge annotated corpus and thus is impractical where manually generatedlabels are not available or are difficult to generate. Unsupervisedlearning methods apply clustering technology to automatically gatherentities from clusters.

For example, a machine learning system 358 may be IBM Watson™ systemthat is an application of advanced natural language processing,information retrieval, knowledge representation and reasoning, andmachine learning technologies to the field of open domain questionanswering. The IBM Watson™ system is built on IBM's DeepQA™ technologyused for hypothesis generation, massive evidence gathering, analysis,and scoring. DeepQA™ takes an input question, analyzes it, decomposesthe question into constituent parts, generates one or more hypothesisbased on the decomposed question and results of a primary search ofanswer sources, performs hypothesis and evidence scoring based on aretrieval of evidence from evidence sources, performs synthesis of theone or more hypothesis, and based on trained models, performs a finalmerging and ranking to output an answer to the input question along witha confidence measure.

As discussed above, one or more of the data storage systems 320 a, 320 b. . . 320 n may be backup data storage systems and/or may include backdata storage locations. As such, the methods and systems described abovefor classification and tagging of metadata corresponding to dataresiding on data storage systems 320 a, 320 b . . . 320 n may also beperformed for backup data residing on the data storage systems 320 a,320 b . . . 320 n. Therefore, the backup data may be classified (usingcustom tags) and/or facets of the backup data may be identified withoutperforming any analysis on the backup data itself (that may requirerestoration of the backup copy from the backup location such as a tapefor analysis), which conserves computing power and resources given theamount of backup data currently stored by enterprises.

For example, the metadata management system may extract event metadatafrom backup operation commands whenever a backup copy of data iscreated, and tag the event metadata corresponding to the backup copy (inaddition to the original data being backed up), as discussed above.Alternatively and/or additionally, the metadata management system mayidentify tags by extracting facets from collected metadata (e.g., eventmetadata or scan metadata obtained by scanning the contents of the datastorage systems) using data miners, and classify the backup data usingthe facets. In yet another embodiment, the metadata management systemmay import metadata tags corresponding to the data being backed up astags for the backup copy created (discussed below with respect to FIG.5). The tags and/or the extracted facets may satisfy particular businessneeds, usage scenarios, user requirements, or the like. For example,metadata associated with backup data of financial enterprises mayinclude tags such as financial information, credit card, personal userinformation, sensitive, confidential, enterprise name, user name, loan,etc. Similarly, extracted facets may include, for example, credit cardnumbers, enterprise names, user names, user address, social securitynumbers, loan application, salary information, or the like.

As such, if a user or client device wants to identify specific backupdata residing in a backup data storage system, for, for example,extraction, access, restoration, or the like, the system does not needto search through copious amounts of backup data stored on the backupdata storage system. Rather, the user may identify the backup data bydefining queries based on the custom tags and/or facets associated withmetadata corresponding to the backup data. For example, a user maydefine a query that executes a search for tags and/or facetscorresponding to certain keywords and a particular enterprise in themetadata store. Once the metadata that satisfies the query isidentified, the system may identify the corresponding backup data as thedata the user is requesting.

Referring now to FIG. 4, an exemplary flowchart in accordance withvarious embodiments illustrating and describing a method forclassification of backup data using metadata is described. While themethod 400 is described for the sake of convenience and not with anintent of limiting the disclosure as comprising a series and/or a numberof steps, it is to be understood that the process does not need to beperformed as a series of steps and/or the steps do not need to beperformed in the order shown and described with respect to FIG. 4 butthe process may be integrated and/or one or more steps may be performedtogether, simultaneously, or the steps may be performed in the orderdisclosed or in an alternate order.

At step 402, the metadata management system may collect initial scanmetadata corresponding to backup data already stored in the one or morebackup data storage systems. Optionally, the metadata may also includesystem metadata. The metadata management system may collect the scanmetadata upon initiation of the metadata management system.

At step 404, the metadata management system may analyze the initial scanmetadata to identify various metadata tags and/or facets to be added tothe metadata and perform an initial classification of the backup dataalready in the one or more backup data storage systems. In one or moreembodiments, the metadata management system may identify the tags basedon user defined policies that include a collection of classificationrules and corresponding metadata tags for classification of metadata. Asdiscussed above, the user may provide the policies via a user interfacesuch as a REST-API. The metadata management system may analyze themetadata (e.g., event metadata) using the user defined policies toidentify various tags to be applied to the metadata. For example, apolicy may include the following classification rule and thecorresponding tag information:

owner:bob AND tag1:U* AND size:[5000 TO 5000]

“tag1”: “myFirstTag”,

“tag2”: 25

The metadata management system may then analyze the received metadatafor “owner:bob AND tag1:U* AND size:[5000 TO 6000]”, and if a match isfound, may apply a tag1 as “myFirstTag” and a tag 2 as “25” to themetadata. In an embodiment, the tag may provide information about thecontent corresponding to the metadata.

In one or more embodiments, the policies for applying the metadata tagsmay correspond to specific business needs, backup levels of associatedcontent (based on, for example, author, included information, filesystem, or the like), or the like. For example, if the owner is aphysician, the tags may include information corresponding to accesslevels of the physician's patient files.

In certain embodiments, the metadata management system may first extractfacets from the collected metadata and then use the extracted facets toclassify the metadata and apply metadata tags. For example, if thefacets include a medical practitioner's name and test reports extractedfrom the metadata of a documents (e.g., from owner information and thefile name information), the metadata management system may identify thecorresponding document to include patient information, and may applytags such as confidential, privileged, patient personal information(PII), medical, or the like. Additionally and/or alternatively, thefacets themselves may be used as tags—for example, the medicalpractitioner's name may be used as a tag to identify all documents thatthe medical practitioner has created/modified.

In certain embodiments, for extracting facets, the metadata managementsystem may create annotators (dictionary based, entity based, custom, orthe like) for classifying metadata stored in the metadata managementsystem. As discussed above, the annotators may be created based on theclassification requirements. For example, if classification is to beperformed to differentiate content associated with different entities orauthors, an annotator may be created that identifies the occurrence ofvarious entity/author names in the metadata. The system may then extractcertain components of the collected metadata. Examples of the extractedcomponents may include, without limitation, file name, file system path,owner information, object name, object bucket/container information,file size, modification history, or any other components that mayprovide information indicative of content corresponding to the metadata.The metadata management system may then extract facets from theextracted components of the metadata by passing the components through aclassifier (e.g., UIMA), as discussed above.

In certain embodiments, the system may analyze the received scanmetadata in first-in, first-out manner. However, other orders foranalyzing the data are within the scope of this disclosure.

The above steps allow for an initial classification of backup dataalready existing backup data storage systems, by identifying custom tagsand/or facets corresponding to metadata, when the metadata managementsystem is initialized.

At step 406, the metadata management system may install the initialmetadata in a database with the identified metadata tag and/or extractedfacets (e.g., as indexes) in an appropriate format. For example, if thedatabase is in the form of a key-value pair noSQL database, the systemmay sort and save the metadata in the form of various key-value pairs asdescribed above. Furthermore, the tag(s) and/or facet(s) may also beapplied as key-value pair(s).

At 408, the metadata management system may collect real-time metadatacorresponding to new backup data stored in the one or more backup datastorage systems. The real-time metadata may include event metadataand/or scan metadata. As discussed above, the metadata management systemmay collect the event metadata (i.e., metadata corresponding a dataoperation executed on a backup data storage system) upon occurrence ofevery new event and/or periodically. Examples of events may includecreation of new backup data in response to a backup operation command,restoration of backup data, archiving, and/or other operations performedin the backup data storage systems. The metadata management system maycollect the event metadata by configuring the backup data storagesystems to send event metadata to the metadata collection system of themetadata management system and/or by monitoring various data operationsexecuted on the backup data storage systems or the network. The metadatamanagement system may collect the scan metadata by periodically scanningthe content of the backup data storage systems.

At 410, the metadata management system may analyze the real-timemetadata to identify various metadata tags and/or facets to be added tothe real-time metadata using, for example, the methods described abovein step 404. In certain embodiments, tags and/or facets for real-timemetadata (particularly event metadata) may also be inherited from tagsand/or facets associated with metadata of the original data (i.e., databeing backed up), when the event is a backup operation (described belowin FIG. 5).

At step 412, the metadata management system may also install thereal-time metadata in the database of step 406 with the identifiedmetadata tag and/or extracted facets (e.g., as indexes) in theappropriate format. In certain embodiments, duplicate information isoverwritten in the database such that it only provides the latestinformation about the backup data residing in the backup data storagesystems. For example, initial metadata may be overwritten by real-timemetadata if it corresponds to the same backup data set but provides moreup to date information about the data set.

The metadata management system may also directly extract facets (414)from the backup data residing on the backup data storage systems (by,for example, using information included in file headers). Tags may alsobe defined based on such directly extracted facets. For example, asdiscussed above, the system may perform supervised learning,unsupervised learning and/or deep inspection methods to extract facetsfrom the backup data. In certain embodiments, extraction of facetsdirectly from the backup data residing on the backup data storagesystems may include restoration of the backup data to, for example, atemporary scratch space in order to perform supervised learning,unsupervised learning and/or deep inspection methods. At 416, the facetsand/or tags are added to the metadata store.

As discussed above, a user may identify backup data using the facetsand/or tags stored in the metadata management system.

Referring now to FIG. 5, an exemplary flowchart in accordance withvarious embodiments illustrating and describing a method of metadata taginheritance during execution of a backup data operation is disclosed.While the method 500 is described for the sake of convenience and notwith an intent of limiting the disclosure as comprising a series and/ora number of steps, it is to be understood that the process does not needto be performed as a series of steps and/or the steps do not need to beperformed in the order shown and described with respect to FIG. 5 butthe process may be integrated and/or one or more steps may be performedtogether, simultaneously, or the steps may be performed in the orderdisclosed or in an alternate order.

At step 502, the metadata management system may collect metadatacorresponding to data sets in a data storage system, and add tags to thecollected metadata, as described above.

At step 504, the metadata management system may monitor the sourcestorage system to detect the execution of a backup command that causescreation of a backup of a data set in a destination location. Forexample, the data storage systems may be configured to send an eventnotification to the metadata management system if a backup operation isexecuted on data residing in the data storage systems. The eventnotification may be sent as a single event (e.g., high level backup)and/or as a series of events (e.g., backup at a file/object level). Theevent notification may include information relating to the backupoperation such as, without limitation, identification of destinationlocation where a backup copy of the data is stored, identification ofthe data set, or the like. In certain embodiments, most commonly usedbackup locations may also be configured to send a notification to themetadata management system if a backup operation is performed to storedata to those locations.

It will be understood to those skilled in the art that movement of databetween storage systems may be caused by certain backup commands suchas, for example and without limitation, BACKUP, ARCHIVE, in conjunctionwith other data operations, or the like.

Upon detection of execution of a backup command, the metadata managementsystem may identify (506) the data set in the source location on whichthe backup is executed. As discussed above, the data storage system maysend event metadata corresponding to the backup executed to the metadatamanagement system. Hence, the metadata management system may retrievethe identity of the data sets by, for example, extracting theinformation from event metadata corresponding to the backup receivedfrom the data storage system and/or from the backup operation command.Alternatively and/or additionally, the data storage system may providethe identity of the data sets to the metadata management system

At 508, the metadata management system may then compare the names of theidentified data set (e.g., file name, object name, etc.) in the sourcelocation and the destination location (506). The metadata managementsystem may identify the name of the data set in the destination locationby querying the data storage system, the backup storage location,extract from the data operation command, or the like. If the names ofthe data set executed upon in the source location and the destinationlocation match (i.e., 508: MATCH), the metadata management system maycreate a new metadata entry in its metadata store corresponding to thedata set in the destination location, and import the metadata and/ormetadata tags from an entry corresponding to the data set in the sourcelocation to the new entry (510). It will be understood to those skilledin the art, that the names are matched to ensure that the contents ofthe data set have not changed between the source and destination systemsand the tags are still applicable. In certain embodiments, in additionto importing the metadata tags in the new entry, the metadata managementsystem may also add new tags corresponding to event metadatacorresponding to the backup executed, using the methods described above.

At 512, if the names of the data set executed upon in the sourcelocation and the destination location do not match (i.e., 508: NOMATCH), the metadata management system may create a new metadata entryin its metadata store corresponding event metadata of the backupexecuted and/or the destination data set. The system may identify newtags for the newly created entries using the methods described above.

At 514, the metadata management system may also perform duplicatedetection across all the data storage systems it manages to determine ifother data sets that are identical to the destination data set exist.The metadata management system may perform duplicate detection using anynow or hereafter known methods. For example, the metadata managementsystem may search for matches for the name of the destination data setin the metadata store and/or the data storage system, and may perform acontent search of the data sets that have a name that is identical tothat of the destination data set.

At 516, if one or more data sets that are identical to the destinationdata set exist (514: YES), the metadata detection system may identifymetadata entries corresponding to each of the one or more data sets, andmay import metadata and/or tags from the metadata entry having theoldest time stamp to the metadata entry created for the destination dataset (i.e., the entry with the newest time stamp).

It will be understood to those skilled in the art that the duplicateidentification and metadata import or inheritance may be performed everytime the metadata collection system receives new event metadata (notjust concerning movement of data between storage systems), and/orperiodically, in order to avoid repetitive processing and tagging ofmetadata for the same content or data sets.

While the illustrative embodiments described above are preferablyimplemented in hardware, such as in units and circuitry of a processor,various aspects of the illustrative embodiments may be implemented insoftware as well. For example, it will be understood that each block ofthe flowchart illustrations in FIGS. 4 and 5, and combinations of blocksin the flowchart illustration, can be implemented by computer programinstructions. These computer program instructions may be provided to aprocessor or other programmable data processing apparatus to produce amachine, such that the instructions which execute on the processor orother programmable data processing apparatus create means forimplementing the functions specified in the flowchart block or blocks.These computer program instructions may also be stored in acomputer-readable memory or storage medium that can direct a processoror other programmable data processing apparatus to function in aparticular manner, such that the instructions stored in thecomputer-readable memory or storage medium produce an article ofmanufacture including instruction means which implement the functionsspecified in the flowchart block or blocks.

Accordingly, blocks of the flowchart illustration support combinationsof means for performing the specified functions, combinations of stepsfor performing the specified functions, and program instruction meansfor performing the specified functions. It will also be understood thateach block of the flowchart illustration, and combinations of blocks inthe flowchart illustration, can be implemented by special purposehardware-based computer systems which perform the specified functions orsteps, or by combinations of special purpose hardware and computerinstructions.

One or more embodiments of the present disclosure may be a system, amethod, and/or a computer program product. The computer program productmay include a computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay be executed substantially concurrently, or the blocks may sometimesbe executed in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustration, and combinations of blocks in the blockdiagrams and/or flowchart illustration, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts or carry out combinations of special purpose hardware and computerinstructions.

Moreover, a system according to various embodiments may include aprocessor and logic integrated with and/or executable by the processor,the logic being configured to perform one or more of the process stepsrecited herein. By integrated with, what is meant is that the processorhas logic embedded therewith as hardware logic, such as an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), etc. By executable by the processor, what is meant is that thelogic is hardware logic; software logic such as firmware, part of anoperating system, part of an application program; etc., or somecombination of hardware and software logic that is accessible by theprocessor and configured to cause the processor to perform somefunctionality upon execution by the processor. Software logic may bestored on local and/or remote memory of any memory type, as known in theart. Any processor known in the art may be used, such as a softwareprocessor module and/or a hardware processor such as an ASIC, a FPGA, acentral processing unit (CPU), an integrated circuit (IC), a graphicsprocessing unit (GPU), etc.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the embodiments of the present disclosure has beenpresented for purposes of illustration and description, but is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the disclosure. The embodiments and examples were chosen anddescribed in order to best explain the principles of the disclosure andthe practical application, and to enable others of ordinary skill in theart to understand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of thedisclosure. However, it should be appreciated that any particularprogram nomenclature herein is used merely for convenience, and thus thedisclosure should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

It will be clear that the various features of the foregoing systemsand/or methodologies may be combined in any way, creating a plurality ofcombinations from the descriptions presented above.

It will be further appreciated that embodiments of the presentdisclosure may be provided in the form of a service deployed on behalfof a customer to offer service on demand.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-executed method for collecting andstoring metadata comprising: maintaining a plurality of backup datastorage systems for receiving from a plurality of data storage systems abackup copy of electronic data stored on the plurality of data storagesystems, the plurality of backup data storage systems storing the backupcopy of electronic data received from the plurality data storage systemsin a different format than the electronic data stored on the pluralityof data storage systems, each of the plurality of backup storage systemshaving one or more processors having circuits and logic for processinginformation and performing logic operations; maintaining the pluralityof backup data storage systems in communication with an externalmetadata management system, wherein the metadata management system hasone or more processors having circuitry and logic for processinginformation and handling logic operations; operating the metadatamanagement system to store tagged metadata corresponding to the backupcopy of electronic data stored on the plurality of backup data storagesystems as one or more entries in the metadata management system,wherein the tagged metadata includes one or more custom tags indicativeof information about the backup copy of electronic data.
 2. The methodof claim 1, wherein operating the metadata management system to storetagged metadata corresponding to the backup copy of electronic datastored on the plurality of backup data storage systems comprises:receiving, by the metadata management system, initial scan metadata fromone or more of the plurality of backup data storage systems; adding, bythe metadata management system, the one or more custom tags to thereceived initial scan metadata to form tagged metadata; and storing thetagged metadata on the metadata management system.
 3. The method ofclaim 2, wherein adding, by the metadata management system, the one ormore custom tags to the received initial scan metadata comprises:receiving, from a user, at least one policy that comprises: a pluralityof classification rules, and at least one custom tag associated witheach of the plurality of classification rules; analyzing, by themetadata management system, the received initial metadata to determineif the received initial scan metadata satisfies one or more of theplurality of classification rules; and in response to determining thatthe received initial scan metadata satisfies one or more of theplurality of classification rules, adding, by the metadata managementsystem, the one or more custom tags associated with each of the one ormore of the plurality of classification rules to the received initialscan metadata.
 4. The method of claim 2, wherein adding, by the metadatamanagement system, the one or more custom tags to the received initialscan metadata comprises: receiving, by the metadata management system,initial scan metadata from one or more of the plurality of backup datastorage systems; extracting, by the metadata management system, one ormore components of the received metadata; using, by the metadatamanagement system, at least one annotator to identify facets for the oneor more components of the received metadata; classifying, by themetadata management system, the metadata based on the identified facets;and adding, by the metadata management system, the one or more customtags to the received metadata.
 5. The method of claim 1, whereinoperating the metadata management system to store tagged metadatacorresponding to the backup copy of electronic data stored on theplurality of backup data storage systems comprises: receiving, by themetadata management system, real-time metadata from one or more of theplurality of backup data storage systems; adding, by the metadatamanagement system, one or more custom tags to the received real-timemetadata to form tagged metadata; and storing the tagged metadata on themetadata management system.
 6. The method of claim 5, wherein receiving,by the metadata management system, real-time metadata comprisesreceiving, by the metadata management system, event metadata uponexecution of a data operation on one or more of the plurality of backupdata storage systems.
 7. The method of claim 1, wherein operating themetadata management system to store tagged metadata corresponding to thebackup copy of electronic data stored on the plurality of backup datastorage systems comprises extracting, by the metadata management system,facets from the backup copy of the electronic data stored on theplurality of backup data storage systems.
 8. The method of claim 7,further comprising: classifying, by the metadata management system, thetagged metadata based on the extracted facets; and adding, by themetadata management system, the one or more custom tags to the taggedmetadata based on the extracted facets.
 9. The method of claim 1,wherein operating the metadata management system to store taggedmetadata corresponding to the backup copy of electronic data residing onthe plurality of backup data storage systems comprises inheriting, bythe metadata management system, tags from metadata associated withoriginal content on the plurality of data storage systems from which thebackup copy of electronic data is created.
 10. The method of claim 9,wherein inheriting tags by the metadata management system comprises:identifying, by the metadata management system, one or more sourcecustom tags included in metadata corresponding to the original contenton the plurality of data storage systems; and in response to detectingexecution of a backup operation on the original content on the pluralityof data storage systems, storing, by the metadata management system, theone or more source custom tags as the one or more custom tags for thebackup copy of the electronic data created by the execution of thebackup operation.
 11. A non-transitory computer readable mediumcomprising programming instructions that when executed cause a processorto: maintain a plurality of backup data storage systems for receivingfrom a plurality of data storage systems a backup copy of electronicdata stored on the plurality of data storage systems, the plurality ofbackup storage systems for storing the backup copy of electronic datareceived from the plurality of data storage devices in a differentformat than the electronic data stored in the plurality of data storagesystems; maintain the plurality of backup data storage systems incommunication with an external metadata management system; operate themetadata management system to store tagged metadata corresponding to thebackup copy of electronic data stored on the plurality of backup datastorage systems as one or more entries in the metadata managementsystem, wherein the tagged metadata includes one or more custom tagsindicative of information about the backup copy of electronic data. 12.The non-transitory computer readable medium of claim 11, wherein causingthe processor to operate the metadata management system to store taggedmetadata corresponding to the backup copy of electronic data stored onthe plurality of backup data storage systems comprises causing theprocessor to: receive, by the metadata management system, initial scanmetadata from one or more of the plurality of backup data storagesystems; add, by the metadata management system, the one or more customtags to the received initial scan metadata to form tagged metadata; andstore the tagged metadata on the metadata management system.
 13. Thenon-transitory computer readable medium of claim 12, wherein causing theprocessor to add, by the metadata management system, the one or morecustom tags to the received initial scan metadata comprises causing theprocessor to: receive, from a user, at least one policy that comprises:a plurality of classification rules, and at least one custom tagassociated with each of the plurality of classification rules; analyzinganalyze, by the metadata management system, the received initialmetadata to determine if the received initial scan metadata satisfiesone or more of the plurality of classification rules; and in response todetermining that the received initial scan metadata satisfies one ormore of the plurality of classification rules, add, by the metadatamanagement system, the one or more custom tags associated with each ofthe one or more of the plurality of classification rules to the receivedinitial scan metadata.
 14. The non-transitory computer readable mediumof claim 12, wherein causing the processor to add, by the metadatamanagement system, one or more custom tags to the received initial scanmetadata comprises causing the processor to: receive, by the metadatamanagement system, initial scan metadata from one or more of theplurality of backup data storage systems; extract, by the metadatamanagement system, one or more components of the received metadata; use,by the metadata management system, at least one annotator to identifyfacets for the one or more components of the received metadata;classify, by the metadata management system, the metadata based on theidentified facets; and add, by the metadata management system, the oneor more custom tags to the received metadata.
 15. The non-transitorycomputer readable medium of claim 11, wherein causing the processor tooperate the metadata management system to store tagged metadatacorresponding to the backup copy of electronic data stored on theplurality of backup data storage systems comprises causing the processorto: receive, by the metadata management system, real-time metadata fromone or more of the plurality of backup data storage systems; add, by themetadata management system, one or more custom tags to the receivedreal-time metadata to form tagged metadata; and store the taggedmetadata on the metadata management system.
 16. The non-transitorycomputer readable medium of claim 15, wherein causing the processor toreceive, by the metadata management system, real-time metadata comprisescausing the processor to receive, by the metadata management system,event metadata upon execution of a data operation on one or more of theplurality of backup data storage systems.
 17. The non-transitorycomputer readable medium of claim 11, wherein causing the processor tooperate the metadata management system to store tagged metadatacorresponding to the backup copy of electronic data stored on theplurality of backup data storage systems comprises causing the processorto: extract, by the metadata management system, facets from the backupcopy of electronic data stored on the plurality of backup data storagesystems.
 18. The non-transitory computer readable medium of claim 17,further comprising causing the processor to: classify, by the metadatamanagement system, the tagged metadata based on the extracted facets;and add, by the metadata management system, the one or more custom tagsto the tagged metadata based upon the extracted facets.
 19. Thenon-transitory computer readable medium of claim 11, wherein causing theprocessor to operate the metadata management system to store taggedmetadata corresponding to the backup copy of electronic data stored onthe plurality of backup data storage systems comprises causing theprocessor to inherit tags from metadata associated with original contenton the plurality of data storage systems from which the backup copy ofelectronic data is created.
 20. The non-transitory computer readablemedium of claim 19, wherein causing the processor to inherit tags, bythe metadata management system, comprises causing the processor to:identify, by the metadata management system, one or more source customtags included in metadata corresponding to the original content on theplurality of data storage systems; and in response to detectingexecution of a backup operation on the original content on the pluralityof data storage systems, store, by the metadata management system, theone or more source custom tags as one or more custom tags for the backupcopy of the electronic data created by the execution of the backupoperation.